Exploring the integration of thermal imaging technology with the data mining algorithms for precise prediction of honey and beeswax yield

Basit Öğe Kaydı
dc.contributor.authorKibar, Mustafa
dc.contributor.authorAltay, Yasin
dc.contributor.authorAytekin, Ibrahim
dc.date.accessioned2024-12-24T19:29:34Z
dc.date.available2024-12-24T19:29:34Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractSustainability in beekeeping depends on identifying the factors affecting honey and beeswax yields (HY and BWY) - key products - and accurately predicting these yields. Therefore, this study aimed to predict HY and BWY using a classification and regression tree (CART), eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, and thermal image processing in Apis mellifera. In this study, 13 colonies of 6 different breeds raised in 10-frame Langstroth hives were used. The effects of independent variables were predicted using data mining algorithms and 15 performance metrics for the effectiveness of the algorithms. Colony power (CP), thermal temperatures (Tmin, Tmax, and Tmean), breed, a*, b*, red, green, saturation, and brightness impacted HY and BWY in different algorithms, but not birth year of queen, L, hue and blue. As a result, XGBoost, CART, and RF demonstrated high predictive performance, respectively. Due to their higher predictive performance, XGBoost and CART algorithms could predict HY and BWY using CP, thermal temperatures, and image values. These techniques could be useful for producers to monitor production quickly and non-invasively without threatening colony welfare.
dc.identifier.doi10.1111/asj.70015
dc.identifier.issn1344-3941
dc.identifier.issn1740-0929
dc.identifier.issue1
dc.identifier.pmid39648138
dc.identifier.scopus2-s2.0-85211103919
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1111/asj.70015
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7128
dc.identifier.volume95
dc.identifier.wosWOS:001371683700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofAnimal Science Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectCART
dc.subjecthoney
dc.subjectrandom forest
dc.subjectthermal imaging
dc.subjectXGBoost
dc.titleExploring the integration of thermal imaging technology with the data mining algorithms for precise prediction of honey and beeswax yield
dc.typeReview Article

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu